Unsupervised Machine learning With Clustering Method

Cases Study Online Retail Clean

Business Understanding

Online Retail Clean merupakan sebuah perusahaan jasa retail, data set dari data transaksi berisi data customer ID, Frequency dan Monetary

Data Understanding

Cutomer ID : adalah ID unik yang dimiliki oleh masing-masing pelanggan
Recency : adalah jumlah hari dari hari terakhir customer membeli ( satuan hari) Frequency : adalah jumlah pembelian yang dilakukan oleh customer ( satuan kali) Monetary : adalah total nilai pembelian dari customer ( satuan Dollar)

Data Preparation

Import Library

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(plotly)

## Loading required package: ggplot2

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

library(factoextra)

## Welcome! Related Books: `Practical Guide To Cluster Analysis in R` at https://goo.gl/13EFCZ

library(cluster)
library(clValid)

Read Data

x<-read.csv('https://raw.githubusercontent.com/arikunco/machinelearning/master/dataset/online_retail_clean.csv')

Cek data

print(summary(x))

##    CustomerID       recency         frequency         monetary        
##  Min.   :12347   Min.   : 21.47   Min.   : 1.000   Min.   :-38970.00  
##  1st Qu.:13752   1st Qu.: 47.43   1st Qu.: 1.000   1st Qu.:    13.20  
##  Median :15249   Median : 92.39   Median : 1.000   Median :    26.10  
##  Mean   :15270   Mean   :133.05   Mean   : 2.259   Mean   :    52.63  
##  3rd Qu.:16792   3rd Qu.:203.47   3rd Qu.: 2.000   3rd Qu.:    56.73  
##  Max.   :18283   Max.   :394.51   Max.   :86.000   Max.   : 10122.56

3D Plot Data Sebelum data dibersihkan

Gunakan R sudio Desktop dengan OS Windows atau gunakan R Server dengan Google Chrome Browser

p <- plot_ly(x, x = ~recency, y = ~frequency, z = ~monetary) %>%
  add_markers() %>%
  layout(scene = list(xaxis = list(title = 'Recency'),
                      yaxis = list(title = 'Frequency'),
                      zaxis = list(title = 'Monetary')))

p

Clean Invalid data

Dari Summary data dan dari plot data terihat bahwa terdapat data monetary dengan nilai <0 secara logika tidak mungkin jumlah pembelian <0 maka data ini di anggap data invalid yang harus dibuang selain data NA juga harus dibuang dari data set

x<-filter(x, monetary >= 0) %>% na.omit(x)
print(summary(x))

##    CustomerID       recency         frequency         monetary       
##  Min.   :12347   Min.   : 21.47   Min.   : 1.000   Min.   :    0.00  
##  1st Qu.:13752   1st Qu.: 47.35   1st Qu.: 1.000   1st Qu.:   13.80  
##  Median :15252   Median : 92.22   Median : 1.000   Median :   27.09  
##  Mean   :15270   Mean   :132.11   Mean   : 2.267   Mean   :   71.20  
##  3rd Qu.:16790   3rd Qu.:200.60   3rd Qu.: 2.000   3rd Qu.:   57.50  
##  Max.   :18283   Max.   :394.51   Max.   :86.000   Max.   :10122.56

Scale Data

Pada metode cluster sangat dipengaruhi jarak antar point pada setiap variable dikarenakan setiap variable memiliki satuan dan skala yang berbeda, maka perlu dilakukan sclaling dari setiap nilai pada variable agar memiliki skala yang sama scale dilakukan dengan menghitung z score dari masing2 nilai variable

x.scale <- as.data.frame(scale(x, scale = TRUE))

head(x.scale)

##    CustomerID   recency   frequency     monetary
## 1 -1.48786478 -1.062717 -0.07696631 -0.008361669
## 2  0.02366284 -1.062383  9.71540290  3.015655286
## 3 -0.47328562 -1.062210  0.21104455 -0.157988362
## 4 -1.58093490 -1.061669  0.49905541  0.158692626
## 5 -0.47615815 -1.061229 -0.36497716 -0.035219272
## 6 -0.79673300 -1.055713  0.21104455 -0.114150785

summary(x.scale)

##    CustomerID           recency          frequency       
##  Min.   :-1.679176   Min.   :-1.0627   Min.   :-0.36498  
##  1st Qu.:-0.872281   1st Qu.:-0.8141   1st Qu.:-0.36498  
##  Median :-0.009946   Median :-0.3831   Median :-0.36498  
##  Mean   : 0.000000   Mean   : 0.0000   Mean   : 0.00000  
##  3rd Qu.: 0.873071   3rd Qu.: 0.6579   3rd Qu.:-0.07697  
##  Max.   : 1.731097   Max.   : 2.5204   Max.   :24.11595  
##     monetary       
##  Min.   :-0.21248  
##  1st Qu.:-0.17128  
##  Median :-0.13162  
##  Mean   : 0.00000  
##  3rd Qu.:-0.04089  
##  Max.   :29.99504

3D Plot

p <- plot_ly(x.scale, x = ~recency, y = ~frequency, z = ~monetary) %>%
  add_markers() %>%
  layout(scene = list(xaxis = list(title = 'Recency'),
                      yaxis = list(title = 'Frequency'),
                      zaxis = list(title = 'Monetary')))

p

Built Model

K-Mean Method

Mencari jumlah cluster optimal

Elbow method

fviz_nbclust(x.scale[,2:4], kmeans, method = "wss") +
  geom_vline(xintercept = 6, linetype = 2)+
  labs(subtitle = "Elbow method")

Silhouette method

fviz_nbclust(x.scale[,2:4], kmeans, method = "silhouette")+
  labs(subtitle = "Silhouette method")

Kedua metode menghasilkan jumlah K optimal k=4

Membuat model k-mean

km.out <- kmeans(x.scale[,2:4], center=4, nstart=10)
x.scale$cluster <- km.out$cluster
x$cluster <- km.out$cluster

Plot hasil clustering

plot(x.scale[,2:4], col = x.scale$cluster,
     main = "K-MEANS of RFM")

3D plot

p <- plot_ly(x.scale, x = ~recency, y = ~frequency, z = ~monetary, color = ~cluster) %>%
  add_markers() %>%
  layout(scene = list(xaxis = list(title = 'Recency'),
                      yaxis = list(title = 'Frequency'),
                      zaxis = list(title = 'Monetary')))

p

K-MEDOIDS (PAM) Method

Mencari Jumlah Cluster Optimal

fviz_nbclust(x.scale[,2:4], pam, method = "silhouette")+
  theme_classic()

jumlah K Optimal adalah k=2

pam.out<-pam(x.scale[,2:4], 2, metric = "euclidean", stand = FALSE)
x.scale$clusterpam <-pam.out$clustering
head(x.scale)

##    CustomerID   recency   frequency     monetary cluster clusterpam
## 1 -1.48786478 -1.062717 -0.07696631 -0.008361669       3          1
## 2  0.02366284 -1.062383  9.71540290  3.015655286       1          1
## 3 -0.47328562 -1.062210  0.21104455 -0.157988362       3          1
## 4 -1.58093490 -1.061669  0.49905541  0.158692626       3          1
## 5 -0.47615815 -1.061229 -0.36497716 -0.035219272       3          1
## 6 -0.79673300 -1.055713  0.21104455 -0.114150785       3          1

Plot hasil clustering

plot(x.scale[,2:4], col = x.scale$clusterpam,
     main = "K-MEDOIDS of RFM")

3D plot

p <- plot_ly(x.scale, x = ~recency, y = ~frequency, z = ~monetary, color = ~clusterpam) %>%
  add_markers() %>%
  layout(scene = list(xaxis = list(title = 'Recency'),
                      yaxis = list(title = 'Frequency'),
                      zaxis = list(title = 'Monetary')))

p

Hirarchical Method

Compute Dissimilarity Matrix

res.dist <- dist(x.scale[,2:4], method = "euclidean")
res.hc <- hclust(d = res.dist, method = "complete")
summary(res.hc)

##             Length Class  Mode     
## merge       4698   -none- numeric  
## height      2349   -none- numeric  
## order       2350   -none- numeric  
## labels      2350   -none- character
## method         1   -none- character
## call           3   -none- call     
## dist.method    1   -none- character

plot(res.hc)

Mencari Algoritma Optimal

clmethods <- c("hierarchical","kmeans","pam")
intr <- clValid(x.scale[,2:4], nClust = 2:6, clMethods = clmethods,validation = "internal" ,maxitems = 2350,metric = "euclidean",method = "complete")
summary(intr)

## 
## Clustering Methods:
##  hierarchical kmeans pam 
## 
## Cluster sizes:
##  2 3 4 5 6 
## 
## Validation Measures:
##                                   2        3        4        5        6
##                                                                        
## hierarchical Connectivity    3.8579  10.4567  13.3151  22.8099  25.0266
##              Dunn            0.5741   0.2713   0.2808   0.2056   0.2504
##              Silhouette      0.9478   0.9077   0.8763   0.8311   0.8039
## kmeans       Connectivity   14.4048  12.6524  65.1853  25.7480 102.0024
##              Dunn            0.0611   0.1926   0.0005   0.0891   0.0017
##              Silhouette      0.9111   0.9066   0.5582   0.7778   0.5400
## pam          Connectivity   48.7032  79.6806  99.9222 156.5710 161.3635
##              Dunn            0.0003   0.0005   0.0003   0.0003   0.0002
##              Silhouette      0.5291   0.3844   0.4448   0.3302   0.3058
## 
## Optimal Scores:
## 
##              Score  Method       Clusters
## Connectivity 3.8579 hierarchical 2       
## Dunn         0.5741 hierarchical 2       
## Silhouette   0.9478 hierarchical 2

optimalScores(intr)

##                  Score       Method Clusters
## Connectivity 3.8579365 hierarchical        2
## Dunn         0.5741036 hierarchical        2
## Silhouette   0.9477798 hierarchical        2

####Diketahui algoritma optimal adalah Hirarki Cluster dengan jumlah cluster = 2

hc.out <- cutree(res.hc, k=2)
x.scale$clusterhc <- hc.out
head(x.scale)

##    CustomerID   recency   frequency     monetary cluster clusterpam
## 1 -1.48786478 -1.062717 -0.07696631 -0.008361669       3          1
## 2  0.02366284 -1.062383  9.71540290  3.015655286       1          1
## 3 -0.47328562 -1.062210  0.21104455 -0.157988362       3          1
## 4 -1.58093490 -1.061669  0.49905541  0.158692626       3          1
## 5 -0.47615815 -1.061229 -0.36497716 -0.035219272       3          1
## 6 -0.79673300 -1.055713  0.21104455 -0.114150785       3          1
##   clusterhc
## 1         1
## 2         1
## 3         1
## 4         1
## 5         1
## 6         1

Plot hasil clustering

plot(x.scale[,2:4], col = x.scale$clusterhc,
     main = "HC of RFM")

3D plot

p <- plot_ly(x.scale, x = ~recency, y = ~frequency, z = ~monetary, color = ~clusterhc) %>%
  add_markers() %>%
  layout(scene = list(xaxis = list(title = 'Recency'),
                      yaxis = list(title = 'Frequency'),
                      zaxis = list(title = 'Monetary')))

p

Iterate Membuang Oulier

Bila dilihat kluster kedua merupakan outlier maka dilakukan perhitungan ulang dengan membuang kluster kedua

x.scale2<-filter(x.scale, monetary <= 16)
intr <- clValid(x.scale2[,2:4], nClust = 2:6, clMethods = clmethods,validation = "internal", maxitems = 2350 ,metric = "euclidean",method = "complete")

## Warning in clValid(x.scale2[, 2:4], nClust = 2:6, clMethods = clmethods, :
## rownames for data not specified, using 1:nrow(data)

summary(intr)

## 
## Clustering Methods:
##  hierarchical kmeans pam 
## 
## Cluster sizes:
##  2 3 4 5 6 
## 
## Validation Measures:
##                                   2        3        4        5        6
##                                                                        
## hierarchical Connectivity    6.5988   9.4571  18.9520  21.1687  29.8460
##              Dunn            0.2713   0.2808   0.2056   0.2504   0.0409
##              Silhouette      0.9077   0.8763   0.8311   0.8039   0.6298
## kmeans       Connectivity    8.7944  61.3274  21.8901  98.1444  87.8675
##              Dunn            0.1926   0.0005   0.0891   0.0017   0.0020
##              Silhouette      0.9067   0.5580   0.7778   0.5398   0.5464
## pam          Connectivity   48.2032  79.1806  99.4222 156.0710 160.8635
##              Dunn            0.0004   0.0007   0.0004   0.0004   0.0003
##              Silhouette      0.5391   0.4009   0.4555   0.3418   0.3174
## 
## Optimal Scores:
## 
##              Score  Method       Clusters
## Connectivity 6.5988 hierarchical 2       
## Dunn         0.2808 hierarchical 3       
## Silhouette   0.9077 hierarchical 2

optimalScores(intr)

##                  Score       Method Clusters
## Connectivity 6.5988095 hierarchical        2
## Dunn         0.2808187 hierarchical        3
## Silhouette   0.9077082 hierarchical        2

res.dist <- dist(x.scale2[,2:4], method = "euclidean")
res.hc <- hclust(d = res.dist, method = "complete")

hc.out <- cutree(res.hc, k=3)
x.scale2$clusterhc <- hc.out
head(x.scale2)

##    CustomerID   recency   frequency     monetary cluster clusterpam
## 1 -1.48786478 -1.062717 -0.07696631 -0.008361669       3          1
## 2  0.02366284 -1.062383  9.71540290  3.015655286       1          1
## 3 -0.47328562 -1.062210  0.21104455 -0.157988362       3          1
## 4 -1.58093490 -1.061669  0.49905541  0.158692626       3          1
## 5 -0.47615815 -1.061229 -0.36497716 -0.035219272       3          1
## 6 -0.79673300 -1.055713  0.21104455 -0.114150785       3          1
##   clusterhc
## 1         1
## 2         2
## 3         1
## 4         1
## 5         1
## 6         1

Plot hasil clustering

plot(x.scale2[,2:4], col = x.scale$clusterhc,
     main = "HC of RFM")

3D plot

p <- plot_ly(x.scale2, x = ~recency, y = ~frequency, z = ~monetary, color = ~clusterhc) %>%
  add_markers() %>%
  layout(scene = list(xaxis = list(title = 'Recency'),
                      yaxis = list(title = 'Frequency'),
                      zaxis = list(title = 'Monetary')))

p

Kesimpulan